scipy.stats.

# sigmaclip#

scipy.stats.sigmaclip(a, low=4.0, high=4.0)[source]#

Perform iterative sigma-clipping of array elements.

Starting from the full sample, all elements outside the critical range are removed, i.e. all elements of the input array c that satisfy either of the following conditions:

```c < mean(c) - std(c)*low
c > mean(c) + std(c)*high
```

The iteration continues with the updated sample until no elements are outside the (updated) range.

Parameters:
aarray_like

Data array, will be raveled if not 1-D.

lowfloat, optional

Lower bound factor of sigma clipping. Default is 4.

highfloat, optional

Upper bound factor of sigma clipping. Default is 4.

Returns:
clippedndarray

Input array with clipped elements removed.

lowerfloat

Lower threshold value use for clipping.

upperfloat

Upper threshold value use for clipping.

Examples

```>>> import numpy as np
>>> from scipy.stats import sigmaclip
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
...                     np.linspace(0, 20, 5)))
>>> fact = 1.5
>>> c, low, upp = sigmaclip(a, fact, fact)
>>> c
array([  9.96666667,  10.        ,  10.03333333,  10.        ])
>>> c.var(), c.std()
(0.00055555555555555165, 0.023570226039551501)
>>> low, c.mean() - fact*c.std(), c.min()
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)
```
```>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
...                     np.linspace(-100, -50, 3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
>>> (c == np.linspace(9.5, 10.5, 11)).all()
True
```